Lung motion estimation from 4D CT using structure-tensor-guided finite-element digital volume correlation.

Objective.Accurate lung motion estimation from 4D computed tomography (CT) is essential for image-guided radiotherapy and thoracic motion analysis, but remains challenging in vessel-rich regions where weak contrast, fine structures, and heterogeneous mechanics limit conventional registration. This work aims to develop an anatomy-informed finite-element digital volume correlation (FE-DVC) framework for accurate and mechanically plausible lung motion estimation.Approach.We propose a structure-tensor-guided heterogeneous anisotropic FE-DVC method. Structure tensors extracted from the reference CT image are used as heuristic anatomical priors to modulate element-wise regularization strength and align an effective anisotropic regularization frame with bronchovascular directions. A lung-specific multi-mesh strategy uses finer elements in vessel-containing regions and coarser elements in parenchyma. A practical workflow based on L-curve analysis and Jacobian-based deformation regularity is introduced to reduce empirical parameter tuning. The method was evaluated on three lung 4D-CT datasets and compared with Demons and pTV registration.Main results.The proposed method consistently reduced landmark target registration error across all datasets, with most errors concentrated below 2 mm and fewer large outliers. In a representative DIR-Lab case, normalized correlation residuals were reduced to 0.059 and 0.100 in axial and coronal views, compared with 0.172/0.256 for Demons and 0.088/0.138 for pTV. The recovered strain fields showed coherent vessel-oriented deformation, and boundary-driven FE simulations confirmed vessel-aligned strain concentrations only under heterogeneous anisotropic regularization. Singular value decomposition further showed that the first motion mode explained more than 90% of deformation energy and the first three modes exceeded 99%.Significance.This framework integrates CT-derived anatomical organization with mechanically interpretable regularization, improving vessel-scale lung motion recovery and supporting future strain-based biomarkers and pulmonary structure-function modeling.